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https://github.com/ocampor/image-quality
Image quality is an open source software library for Image Quality Assessment (IQA).
https://github.com/ocampor/image-quality
artificial-intelligence computer-vision image-analysis image-quality-assessment machine-learning python tensorflow
Last synced: about 2 months ago
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Image quality is an open source software library for Image Quality Assessment (IQA).
- Host: GitHub
- URL: https://github.com/ocampor/image-quality
- Owner: ocampor
- License: apache-2.0
- Created: 2019-07-06T16:28:23.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2024-02-05T19:24:49.000Z (11 months ago)
- Last Synced: 2024-11-09T05:13:15.321Z (2 months ago)
- Topics: artificial-intelligence, computer-vision, image-analysis, image-quality-assessment, machine-learning, python, tensorflow
- Language: Python
- Homepage:
- Size: 12.7 MB
- Stars: 399
- Watchers: 10
- Forks: 86
- Open Issues: 20
-
Metadata Files:
- Readme: README.rst
- Contributing: CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
.. -*- mode: rst -*-
|Travis|_ |PyPi|_
.. |Travis| image:: https://travis-ci.com/ocampor/image-quality.svg?branch=master
.. _Travis: https://travis-ci.com/ocampor/image-quality.. |PyPi| image:: https://img.shields.io/pypi/dm/image-quality?color=blue :alt: PyPI - Downloads
.. _PyPi: https://pypi.org/project/image-quality/Image Quality
=============Description
-----------Image quality is an open source software library for Automatic Image
Quality Assessment (IQA).Dependencies
------------- Python 3.8
- (Development) DockerInstallation
------------The package is public and is hosted in PyPi repository. To install it in
your machine run::
pip install image-quality
Example
-------After installing ``image-quality`` package, you can test that it was
successfully installed running the following commands in a python
terminal.::
>>> import imquality.brisque as brisque
>>> import PIL.Image>>> path = 'path/to/image'
>>> img = PIL.Image.open(path)
>>> brisque.score(img)
4.9541572815704455Development
-----------In case of adding a new tensorflow dataset or modifying the location of a zip file, it is
necessary to update the url checksums. You can find the instructions in the following
`tensorflow documentation `_.The steps to create the url checksums are the following:
1. Take the file with the dataset configuration (e.g. live_iqa.py) an place it in the ``tensorflow_datasets``
folder. The folder is commonly placed in ``${HOME}/.local/lib/python3.8/site-packages`` if you
install the python packages using the ``user`` flag.2. Modify the ``__init__.py`` of the ``tensorflow_datasets`` to import your new dataset.
For example ``from .image.live_iqa import LiveIQA`` at the top of the file.3. In your terminal run the commands:
::touch url_checksums/live_iqa.txt
python -m tensorflow_datasets.scripts.download_and_prepare \
--register_checksums \
--datasets=live_iqa4. The file ``live_iqa.txt`` is going to contain the checksum. Now you can copy and paste it to your
project's ``url_checksums`` folder.Sponsor
-------.. image:: https://github.com/antonreshetov/mysigmail/raw/master/jetbrains.svg?sanitize=true
:target: _Maintainer
----------- `Ricardo Ocampo `_